簡易檢索 / 詳目顯示

研究生: 蘇紹悌
Sathishkumar Subburaj
論文名稱: 使用PVDF/石墨烯壓電感測器及基於K-mer的機器學習方法 於即時探測與辨識
In-Situ Detection and Classification Using PVDF/Graphene Piezoelectric Sensors with K-mer-based Machine Learning Methods
指導教授: 林柏廷
Po-Ting Lin
口試委員: 張敬源
Ching-Yuan Chang
洪維松
Wei-Song Hung
徐冠倫
Kuan-Lun Hsu
陳羽薰
Yu-Hsun Chen
學位類別: 博士
Doctor
系所名稱: 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 英文
論文頁數: 142
中文關鍵詞: 結合強度PVDF/Gr 複合薄膜彎曲傳感器螺旋傳感器K-mer
外文關鍵詞: Bonding strength, PVDF/Gr composite film, Curved sensor, Helical sensor, K-mer
相關次數: 點閱:203下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 近年來,柔性傳感器的發展因其在醫療保健、機器人、環境監測和可穿戴技術等各個領域的潛在應用而受到廣泛關注。與傳統的剛性傳感器不同,柔性傳感器具有靈活性、輕量化設計以及多功能等優點。這些傳感器可以無縫地整合到各種曲面物體、紡織品甚至人體部位上,從而實現各種創新和非侵入式感測應用。在本文中,我們報告了柔性壓電傳感器的設計和製造,其中高度排列的聚偏二氟乙烯(PVDF)/石墨烯(Gr)複合膜被引入作為壓電活性元件並通過銅電極連接。將高負載量的 Gr 填料加入到 PVDF 基體中會引起 PVDF 鏈的自組裝,從而產生顯著的 相含量和獨特的壓電性能。該曲面傳感器表現出 0.35VN-1 的出色靈敏度,這是可穿戴傳感器的關鍵性能指標。這種彎曲的傳感器設計可以輕鬆連接到各個身體部位,以跟踪人體部位的運動。此外,我們還開發了一種創新的螺旋壓電傳感器,用於檢測多維負載。螺旋傳感器被製成螺旋帶的形狀,當它發生變形時,它會產生不同的信號模式。為了對傳感器數據進行分類,我們採用了結合 K-mer 的機器學習算法,這種方法的準確率超過 90%。這些柔性壓電傳感設備的製造過程簡單、高效且通用,並且我們的測試取得了令人期待的結果.


    Recently, the development of flexible sensors has gained significant attention due to their potential applications in various fields, including healthcare, robotics, environmental monitoring, and wearable technology. Unlike traditional rigid sensors, flexible sensors offer advantages such as flexibility, lightweight design, and versatility of substrate. These sensors can be seamlessly integrated into various surfaces, textiles, or even human body parts, enabling a wide range of innovative and non-intrusive sensing applications. In this paper, we report on the design and fabrication of a flexible piezoelectric sensor, where highly aligned Polyvinylidene fluoride (PVDF)/Graphene (Gr) composite film is introduced as the piezoelectric active component and attached by copper electrodes. Incorporating a high-loading quantity of Gr fillers into a PVDF matrix induced the self-assembly of PVDF chains, resulting in a significant -phase content and unique piezoelectric properties. The curved sensor exhibited excellent mechanical sensitivity of 0.35VN-1 is a key performance metric for wearable sensors. This curved sensor design enables easy attachment to various body parts to track human body parts movements. Additionally, we developed an innovative helical piezoelectric sensor for detecting multi-dimensional oads. The helical sensor was prepared in the shape of a helical strip, and as it underwent deformation, it generated distinct signal patterns. To classify the sensor data, we employed a machine learning algorithm that incorporated K-mer and this approach yielded an accuracy of over 90%. The fabrication process for these flexible piezoelectric sensing devices is simple, efficient, and versatile, and our tests yielded promising results.

    Table of content Abstract i Acknowledgment iii Table of content v List of tables vii List of figures vii Nomenclature xiii Abbreviation xiv 1. Introduction 1 2. Literature review 13 2.1. Tensile test and shear test 13 2.2. Peel Test 14 2.3. Blister Test 15 2.4. Flexible sensor 15 2.5. Development of the piezoelectric β-phase 18 2.6. Fillers 19 2.7. HAR 20 2.8. K-mer 22 2.9. Summary 23 3. Materials and methods 25 3.1. Materials 25 3.1.1. PDMS 25 3.1.2. Ecoflex 26 3.1.3. PVDF 27 3.1.4. Graphene 28 3.2. Soft composite membrane preparation 29 3.2.1. Material preparation for blister test 29 3.2.2. Experimental setup for blister test 31 3.3. Fabrication process of the PVDF/Gr sensor 31 3.3.1. PVDF/Gr solution preparation 31 3.3.2. PVDF/Gr-based Curved Sensor (PGCS) fabrication process 32 3.3.3. Helical sensor fabrication process 33 3.3.4. PVDF/Gr composite film characterization 35 3.3.5. Experimental setup for PVDF/Gr-based sensors 36 3.4. Sensor data classification based on machine learning techniques 37 3.4.1. Machine learning in wearable sensor 37 3.4.2. Pattern recognition 38 3.4.3. Support Vector Machine (SVM) 39 3.4.4. Random Forest (RF) 40 3.4.5. k-Nearest Neighbors (k-NN) 42 3.4.6. K-fold cross-validation 44 3.4.7. K-mer 45 3.4.8. Evaluation index 46 4. Results and Discussion 50 4.1. Soft composite materials bonding strength measurement 50 4.1.1. Force model 50 4.1.2. Bonding strength analysis 53 4.2. Morphological and physicochemical characterization of PVDF/Gr composite film. 57 4.3. Piezoelectric theory of d33 and d31 mode 61 4.4. Sensing performance of PGCS 63 4.4.1. Finite Element Analysis (FEA) for calculation of deformation 66 4.4.2. Human body parts motion tracking 70 4.4.3. Curved sensor data collection 73 4.4.4. Human gestures dataset classification 84 4.5. Helical sensor data collection 89 4.5.1. Multi-dimensional load classification 97 4.6. Helical sensor standardization and stability test with uArm 103 5. Conclusion 111 References 113 Curriculum Vitae 125

    [1] G. Agarwal, N. Besuchet, B. Audergon, and J. Paik, Stretchable Materials for Robust Soft Actuators towards Assistive Wearable Devices, Sci Rep, 6, p. 34224, 2016. doi: 10.1038/srep34224
    [2] H. R. Lim, H. S. Kim, R. Qazi, Y. T. Kwon, J. W. Jeong, and W. H. Yeo, Advanced Soft Materials, Sensor Integrations, and Applications of Wearable Flexible Hybrid Electronics in Healthcare, Energy, and Environment, Adv Mater, 32, (15), p. e1901924, 2020. doi: 10.1002/adma.201901924
    [3] A. Miriyev, K. Stack, and H. Lipson, Soft material for soft actuators, Nat Commun, 8, (1), p. 596, 2017. doi: 10.1038/s41467-017-00685-3
    [4] J. Park, J. Li, and A. Han, Micro-macro hybrid soft-lithography master (MMHSM) fabrication for lab-on-a-chip applications, Biomed Microdevices, 12, (2), pp. 345-51, 2010. doi: 10.1007/s10544-009-9390-9
    [5] L. Lunelli, F. Barbaresco, G. Scordo, C. Potrich, L. Vanzetti, S. L. Marasso, M. Cocuzza, C. F. Pirri, and C. Pederzolli, PDMS-Based Microdevices for the Capture of MicroRNA Biomarkers, Appl Sci, 10, (11), 2020. doi: 10.3390/app10113867
    [6] F. Boulogne, S. Khodaparast, C. Poulard, and H. A. Stone, Protocol to perform pressurized blister tests on thin elastic films, Eur Phys J E Soft Matter, 40, (6), p. 64, 2017. doi: 10.1140/epje/i2017-11553-1
    [7] S. C. J. Wong, H. Na, and P. Chen, Measurement of adhesion energy of electrospun polymer membranes using a shaft-loaded blister test, presented at the 13th International Conference on Fracture Beijing, China, June 16–21,, 2013.
    [8] H. Na, P. Chen, K. T. Wan, S. C. Wong, Q. Li, and Z. Ma, Measurement of adhesion work of electrospun polymer membrane by shaft-loaded blister test, Langmuir, 28, (16), pp. 6677-83, 2012. doi: 10.1021/la300877r
    [9] S. Bauer, S. Bauer-Gogonea, I. Graz, M. Kaltenbrunner, C. Keplinger, and R. Schwodiauer, 25th anniversary article: A soft future: from robots and sensor skin to energy harvesters, Adv Mater, 26, (1), pp. 149-61, 2014. doi: 10.1002/adma.201303349
    [10] Y. Liu, K. He, G. Chen, W. R. Leow, and X. Chen, Nature-Inspired Structural Materials for Flexible Electronic Devices, Chem Rev, 117, (20), pp. 12893-12941, 2017. doi: 10.1021/acs.chemrev.7b00291
    [11] S. K. Ghosh and D. Mandal, Bio-assembled, piezoelectric prawn shell made self-powered wearable sensor for non-invasive physiological signal monitoring, Appl Phys Lett, 110, (12), 2017. doi: 10.1063/1.4979081
    [12] N. R. Alluri, B. Saravanakumar, and S. J. Kim, Flexible, Hybrid Piezoelectric Film (BaTi(1-x)Zr(x)O3)/PVDF Nanogenerator as a Self-Powered Fluid Velocity Sensor, ACS Appl Mater Interfaces, 7, (18), pp. 9831-40, 2015. doi: 10.1021/acsami.5b01760
    [13] Y. Mao, D. Geng, E. Liang, and X. Wang, Single-electrode triboelectric nanogenerator for scavenging friction energy from rolling tires, Nano Energy, 15, pp. 227-234, 2015. doi: 10.1016/j.nanoen.2015.04.026
    [14] S. Lee, E. K. Lee, E. Lee, and G. Y. Bae, Transparent and Flexible Vibration Sensor Based on a Wheel-Shaped Hybrid Thin Membrane, Micromachines (Basel), 12, (10), 2021. doi: 10.3390/mi12101246
    [15] Y. Zhao, S. Gao, X. Zhang, W. Huo, H. Xu, C. Chen, J. Li, K. Xu, and X. Huang, Fully Flexible Electromagnetic Vibration Sensors with Annular Field Confinement Origami Magnetic Membranes, Adv Funct Mater, 30, (25), 2020. doi: 10.1002/adfm.202001553
    [16] Y. Zhou, P. Zhan, M. Ren, G. Zheng, K. Dai, L. Mi, C. Liu, and C. Shen, Significant Stretchability Enhancement of a Crack-Based Strain Sensor Combined with High Sensitivity and Superior Durability for Motion Monitoring, ACS Appl Mater Interfaces, 11, (7), pp. 7405-7414, 2019. doi: 10.1021/acsami.8b20768
    [17] M. Xu, F. Li, Z. Zhang, T. Shen, Q. Zhang, and J. Qi, Stretchable and multifunctional strain sensors based on 3D graphene foams for active and adaptive tactile imaging, Sci China Mater, 62, (4), pp. 555-565, 2018. doi: 10.1007/s40843-018-9348-8
    [18] X. Wang, R. Guo, B. Yuan, Y. Yao, F. Wang, and J. Liu, Ni-doped Liquid Metal Printed Highly Stretchable and Conformable Strain Sensor for Multifunctional Human-Motion Monitoring, Annu Int Conf IEEE Eng Med Biol Soc, 2018, pp. 3276-3279, 2018. doi: 10.1109/EMBC.2018.8513060
    [19] Y. Yu, Y. Luo, A. Guo, L. Yan, Y. Wu, K. Jiang, Q. Li, S. Fan, and J. Wang, Flexible and transparent strain sensors based on super-aligned carbon nanotube films, Nanoscale, 9, (20), pp. 6716-6723, 2017. doi: 10.1039/c6nr09961k
    [20] J. A. Juárez-Moreno, A. Ávila-Ortega, A. I. Oliva, F. Avilés, and J. V. Cauich-Rodríguez, Effect of wettability and surface roughness on the adhesion properties of collagen on PDMS films treated by capacitively coupled oxygen plasma, Appl Surf Sci, 349, pp. 763-773, 2015. doi: 10.1016/j.apsusc.2015.05.063
    [21] D. Chen, F. Chen, X. Hu, H. Zhang, X. Yin, and Y. Zhou, Thermal stability, mechanical and optical properties of novel addition cured PDMS composites with nano-silica sol and MQ silicone resin, Composites Sci Technol, 117, pp. 307-314, 2015. doi: 10.1016/j.compscitech.2015.07.003
    [22] I. D. Johnston, D. K. McCluskey, C. K. L. Tan, and M. C. Tracey, Mechanical characterization of bulk Sylgard 184 for microfluidics and microengineering, J Micromech Microeng, 24, (3), 2014. doi: 10.1088/0960-1317/24/3/035017
    [23] X. Wang, Y. Gu, Z. Xiong, Z. Cui, and T. Zhang, Silk-molded flexible, ultrasensitive, and highly stable electronic skin for monitoring human physiological signals, Adv Mater, 26, (9), pp. 1336-42, 2014. doi: 10.1002/adma.201304248
    [24] X. Zhang, H. K. Chu, Y. Zhang, G. Bai, K. Wang, Q. Tan, and D. Sun, Rapid characterization of the biomechanical properties of drug-treated cells in a microfluidic device, J Micromech Microeng, 25, (10), 2015. doi: 10.1088/0960-1317/25/10/105004
    [25] S. Singh, N. Kumar, D. George, and A. K. Sen, Analytical modeling, simulations and experimental studies of a PZT actuated planar valveless PDMS micropump, Sens Actuator A Phys, 225, pp. 81-94, 2015. doi: 10.1016/j.sna.2015.02.012
    [26] Y. Tao, F. Han, C. Shi, R. Yang, Y. Chen, and Y. Ren, Liquid Metal-Based Flexible and Wearable Sensor for Functional Human-Machine Interface, Micromachines (Basel), 13, (9), 2022. doi: 10.3390/mi13091429
    [27] Q. Du, L. Liu, R. Tang, J. Ai, Z. Wang, Q. Fu, C. Li, Y. Chen, and X. Feng, High‐Performance Flexible Pressure Sensor Based on Controllable Hierarchical Microstructures by Laser Scribing for Wearable Electronics, Adv Mater Technol, 6, (9), 2021. doi: 10.1002/admt.202100122
    [28] S. K. Karan, R. Bera, S. Paria, A. K. Das, S. Maiti, A. Maitra, and B. B. Khatua, An Approach to Design Highly Durable Piezoelectric Nanogenerator Based on Self-Poled PVDF/AlO-rGO Flexible Nanocomposite with High Power Density and Energy Conversion Efficiency, Adv Energy Mater, 6, (20), 2016. doi: 10.1002/aenm.201601016
    [29] T. Y. Choi, B. U. Hwang, B. Y. Kim, T. Q. Trung, Y. H. Nam, D. N. Kim, K. Eom, and N. E. Lee, Stretchable, Transparent, and Stretch-Unresponsive Capacitive Touch Sensor Array with Selectively Patterned Silver Nanowires/Reduced Graphene Oxide Electrodes, ACS Appl Mater Interfaces, 9, (21), pp. 18022-18030, 2017. doi: 10.1021/acsami.6b16716
    [30] L. T. Duy, J.-Y. Baek, Y.-J. Mun, and H. Seo, Patternable production of SrTiO3 nanoparticles using 1-W laser directly on flexible humidity sensor platform based on ITO/SrTiO3/CNT, J Mater Sci Technol, 71, pp. 186-194, 2021. doi: 10.1016/j.jmst.2020.07.024
    [31] H. Li, K. Wu, Z. Xu, Z. Wang, Y. Meng, and L. Li, Ultrahigh-Sensitivity Piezoresistive Pressure Sensors for Detection of Tiny Pressure, ACS Appl Mater Interfaces, 10, (24), pp. 20826-20834, 2018. doi: 10.1021/acsami.8b03639
    [32] Y. Wu, I. Karakurt, L. Beker, Y. Kubota, R. Xu, K. Y. Ho, S. Zhao, J. Zhong, M. Zhang, X. Wang, and L. Lin, Piezoresistive stretchable strain sensors with human machine interface demonstrations, Sens Actuator A Phys, 279, pp. 46-52, 2018. doi: 10.1016/j.sna.2018.05.036
    [33] Q. Liu, X. X. Wang, W. Z. Song, H. J. Qiu, J. Zhang, Z. Fan, M. Yu, and Y. Z. Long, Wireless Single-Electrode Self-Powered Piezoelectric Sensor for Monitoring, ACS Appl Mater Interfaces, 12, (7), pp. 8288-8295, 2020. doi: 10.1021/acsami.9b21392
    [34] K. Maity, S. Garain, K. Henkel, D. Schmeißer, and D. Mandal, Self-Powered Human-Health Monitoring through Aligned PVDF Nanofibers Interfaced Skin-Interactive Piezoelectric Sensor, ACS Appl Polym Mater, 2, (2), pp. 862-878, 2020. doi: 10.1021/acsapm.9b00846
    [35] Z. Liu, Z. Zhao, X. Zeng, X. Fu, and Y. Hu, Expandable microsphere-based triboelectric nanogenerators as ultrasensitive pressure sensors for respiratory and pulse monitoring, Nano Energy, 59, pp. 295-301, 2019. doi: 10.1016/j.nanoen.2019.02.057
    [36] Y.-E. Shin, J.-E. Lee, Y. Park, S.-H. Hwang, H. G. Chae, and H. Ko, Sewing machine stitching of polyvinylidene fluoride fibers: programmable textile patterns for wearable triboelectric sensors, J Mater Chem A, 6, (45), pp. 22879-22888, 2018. doi: 10.1039/c8ta08485h
    [37] Y. Zang, F. Zhang, C.-a. Di, and D. Zhu, Advances of flexible pressure sensors toward artificial intelligence and health care applications, Mater Horizons, 2, (2), pp. 140-156, 2015. doi: 10.1039/c4mh00147h
    [38] F. R. Fan, W. Tang, and Z. L. Wang, Flexible Nanogenerators for Energy Harvesting and Self-Powered Electronics, Adv Mater, 28, (22), pp. 4283-305, 2016. doi: 10.1002/adma.201504299
    [39] C. Dagdeviren, P. Joe, O. L. Tuzman, K.-I. Park, K. J. Lee, Y. Shi, Y. Huang, and J. A. Rogers, Recent progress in flexible and stretchable piezoelectric devices for mechanical energy harvesting, sensing and actuation, Extreme Mech Lett, 9, pp. 269-281, 2016. doi: 10.1016/j.eml.2016.05.015
    [40] B. S. Ince-Gunduz, R. Alpern, D. Amare, J. Crawford, B. Dolan, S. Jones, R. Kobylarz, M. Reveley, and P. Cebe, Impact of nanosilicates on poly(vinylidene fluoride) crystal polymorphism: Part 1. Melt-crystallization at high supercooling, Polymer, 51, (6), pp. 1485-1493, 2010. doi: 10.1016/j.polymer.2010.01.011
    [41] A. J. Lovinger, Poly (vinylidene fluoride), Developments in crystalline polymers—1, pp. 195-273, 1982.
    [42] T. Wu, H. Jin, S. Dong, W. Xuan, H. Xu, L. Lu, Z. Fang, S. Huang, X. Tao, L. Shi, S. Liu, and J. Luo, A Flexible Film Bulk Acoustic Resonator Based on -Phase Polyvinylidene Fluoride Polymer, Sensors (Basel), 20, (5), 2020. doi: 10.3390/s20051346
    [43] T. Huang, S. Yang, P. He, J. Sun, S. Zhang, D. Li, Y. Meng, J. Zhou, H. Tang, J. Liang, G. Ding, and X. Xie, Phase-Separation-Induced PVDF/Graphene Coating on Fabrics toward Flexible Piezoelectric Sensors, ACS Appl Mater Interfaces, 10, (36), pp. 30732-30740, 2018. doi: 10.1021/acsami.8b10552
    [44] S. Garain, S. Jana, T. K. Sinha, and D. Mandal, Design of In Situ Poled Ce(3+)-Doped Electrospun PVDF/Graphene Composite Nanofibers for Fabrication of Nanopressure Sensor and Ultrasensitive Acoustic Nanogenerator, ACS Appl Mater Interfaces, 8, (7), pp. 4532-40, 2016. doi: 10.1021/acsami.5b11356
    [45] S. Chen, X. Li, K. Yao, F. E. H. Tay, A. Kumar, and K. Zeng, Self-polarized ferroelectric PVDF homopolymer ultra-thin films derived from Langmuir–Blodgett deposition, Polymer, 53, (6), pp. 1404-1408, 2012. doi: 10.1016/j.polymer.2012.01.058
    [46] S. Jana, S. Garain, S. K. Ghosh, S. Sen, and D. Mandal, The preparation of gamma-crystalline non-electrically poled photoluminescant ZnO-PVDF nanocomposite film for wearable nanogenerators, Nanotechnology, 27, (44), p. 445403, 2016. doi: 10.1088/0957-4484/27/44/445403
    [47] A. P. Indolia and M. S. Gaur, Investigation of structural and thermal characteristics of PVDF/ZnO nanocomposites, J Therm Anal Calorim, 113, (2), pp. 821-830, 2012. doi: 10.1007/s10973-012-2834-0
    [48] H. Parangusan, D. Ponnamma, and M. A. A. AlMaadeed, Toward High Power Generating Piezoelectric Nanofibers: Influence of Particle Size and Surface Electrostatic Interaction of Ce-Fe(2)O(3) and Ce-Co(3)O(4) on PVDF, ACS Omega, 4, (4), pp. 6312-6323, 2019. doi: 10.1021/acsomega.9b00243
    [49] S. Kumar, V. S. Manikandan, A. K. Palai, S. Mohanty, and S. K. Nayak, Fe2O3 as an efficient filler in PVDF-HFP based polymeric electrolyte for dye sensitized solar cell application, Solid State Ion, 332, pp. 10-15, 2019. doi: 10.1016/j.ssi.2019.01.006
    [50] M. El Achaby, F. Z. Arrakhiz, S. Vaudreuil, E. M. Essassi, and A. Qaiss, Piezoelectric β-polymorph formation and properties enhancement in graphene oxide – PVDF nanocomposite films, Appl Surf Sci, 258, (19), pp. 7668-7677, 2012. doi: 10.1016/j.apsusc.2012.04.118
    [51] A. Islam, A. N. Khan, M. F. Shakir, and K. Islam, Strengthening of β polymorph in PVDF/FLG and PVDF/GO nanocomposites, Materials Research Express, 7, (1), 2019. doi: 10.1088/2053-1591/ab5f82
    [52] B. Jaleh and A. Jabbari, Evaluation of reduced graphene oxide/ZnO effect on properties of PVDF nanocomposite films, Appl Surf Sci, 320, pp. 339-347, 2014. doi: 10.1016/j.apsusc.2014.09.030
    [53] M. I. Mohammed, S. S. Fouad, and N. Mehta, Dielectric relaxation and thermally activated a.c. conduction in (PVDF)/(rGO) nano-composites: role of rGO over different fillers, J Mater Sci Mater Electron, 29, (21), pp. 18271-18281, 2018. doi: 10.1007/s10854-018-9941-z
    [54] H. Yu, T. Huang, M. Lu, M. Mao, Q. Zhang, and H. Wang, Enhanced power output of an electrospun PVDF/MWCNTs-based nanogenerator by tuning its conductivity, Nanotechnology, 24, (40), p. 405401, 2013. doi: 10.1088/0957-4484/24/40/405401
    [55] A. Saha, T. Sharma, H. Batra, A. Jain, and V. Pal, Human action recognition using smartphone sensors, International Conference on Computational Performance Evaluation (ComPE), pp. 238-243, 2020.
    [56] L. Chen, J. Hoey, C. D. Nugent, D. J. Cook, and Z. Yu, Sensor-based activity recognition, IEEE Transactions on Systems, Man,Cybernetics, Part C, 42, (6), pp. 790-808, 2012.
    [57] K Muralidharan, Ramesh Anirudh, G Rithvik, S. Prem, R. A A, and D. M. P. Gopinath, 1D Convolution approach to human activity recognition using sensor data and comparison with machine learning algorithms, International Journal of Cognitive Computing in Engineering, 2, pp. 130-143, 2021. doi: 10.1016/j.ijcce.2021.09.001
    [58] N. U. Ahamed, D. Kobsar, L. C. Benson, C. A. Clermont, S. T. Osis, and R. Ferber, Subject-specific and group-based running pattern classification using a single wearable sensor, J Biomech, 84, pp. 227-233, 2019. doi: 10.1016/j.jbiomech.2019.01.001
    [59] Y. Zhan and T. Kuroda, Wearable sensor-based human activity recognition from environmental background sounds, J Ambient Intell Humaniz Comput, 5, (1), pp. 77-89, 2012. doi: 10.1007/s12652-012-0122-2
    [60] E. Brophy, W. Muehlhausen, A. F. Smeaton, and T. E. Ward, Cnns for heart rate estimation and human activity recognition in wrist worn sensing applications, IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 1-6, 2020.
    [61] S. U. Park, J. H. Park, M. A. Al-masni, M. A. Al-antari, M. Z. Uddin, and T. S. Kim, A Depth Camera-based Human Activity Recognition via Deep Learning Recurrent Neural Network for Health and Social Care Services, Procedia Comput Sci, 100, pp. 78-84, 2016. doi: 10.1016/j.procs.2016.09.126
    [62] M. H. Siddiqi, N. Almashfi, A. Ali, M. Alruwaili, Y. Alhwaiti, S. Alanazi, and M. M. Kamruzzaman, A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera, IEEE Access, 9, pp. 92300-92317, 2021. doi: 10.1109/access.2021.3092403
    [63] E. Bulbul, A. Cetin, and I. A. Dogru, Human activity recognition using smartphones, 2nd international symposium on multidisciplinary studies and innovative technologies (ismsit), pp. 1-6, 2018.
    [64] B. M. H. Abidine, L. Fergani, B. Fergani, and M. Oussalah, The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition, Pattern Anal Appl, 21, (1), pp. 119-138, 2016. doi: 10.1007/s10044-016-0570-y
    [65] C. Hu, Y. Chen, L. Hu, and X. Peng, A novel random forests based class incremental learning method for activity recognition, Pattern Recognit, 78, pp. 277-290, 2018. doi: 10.1016/j.patcog.2018.01.025
    [66] A. Murad and J. Y. Pyun, Deep Recurrent Neural Networks for Human Activity Recognition, Sensors (Basel), 17, (11), 2017. doi: 10.3390/s17112556
    [67] H. Gjoreski, J. Bizjak, M. Gjoreski, and M. Gams, Comparing deep and classical machine learning methods for human activity recognition using wrist accelerometer, Proceedings of the IJCAI 2016 Workshop on Deep Learning for Artificial Intelligence, New York, NY, USA, vol. 10, p. 970, 2016.
    [68] O. D. Lara and M. A. Labrador, A survey on human activity recognition using wearable sensors, IEEE communications surveys, 15, (3), pp. 1192-1209, 2012.
    [69] R. Poppe, A survey on vision-based human action recognition, Image Vision Comput, 28, (6), pp. 976-990, 2010. doi: 10.1016/j.imavis.2009.11.014
    [70] D. Mapleson, G. Garcia Accinelli, G. Kettleborough, J. Wright, and B. J. Clavijo, KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies, Bioinformatics, 33, (4), pp. 574-576, 2017. doi: 10.1093/bioinformatics/btw663
    [71] F. P. Breitwieser, D. N. Baker, and S. L. Salzberg, KrakenUniq: confident and fast metagenomics classification using unique k-mer counts, Genome Biol, 19, (1), p. 198, 2018. doi: 10.1186/s13059-018-1568-0
    [72] H. Sun, J. Ding, M. Piednoel, and K. Schneeberger, findGSE: estimating genome size variation within human and Arabidopsis using k-mer frequencies, Bioinformatics, 34, (4), pp. 550-557, 2018. doi: 10.1093/bioinformatics/btx637
    [73] C. H. Yeh, S. Subburaj, W. S. Hung, C. Y. Chang, and P. T. Lin, Classification of Piezoelectric Signals from PVDF/Graphene Membrane Sensors Using K-mer-based Sensing Recognition (KSR), The 45th National Conference on Theoretical and Applied Mechanics (CTAM 2021),Taipei, Taiwan, 2021.
    [74] Y. T. Yao, Y. W. Wu, and P. T. Lin, K-mer-based Pattern Recognition (KPR) for the Keyboard Inspection, 20th World Congress on Non-Destructive Testing (WCNDT 2020),Seoul, Korea, pp. A20191001-0276, 2020.
    [75] Y. T. Yao, Y. W. Wu, and P. T. Lin, A two-stage multi-fidelity design optimization for K-mer-based pattern recognition (KPR) in image processing, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference(IDETC/CIE 2020),St. Louis, MO, USA, vol. 84010, p. V11BT11A031, 2020.
    [76] Y.-T. Yao and P. T. Lin, Multi-Fidelity Design Optimization for K-mer-based Pattern Recognition (KPR) of Handwritten Characters, Asian Congress of Structural and Multidisciplinary Optimization 2020 (ACSMO 2020), p. P00267, 2020.
    [77] R. Sivakumar and N. Y. Lee, Chemically robust succinimide-group-assisted irreversible bonding of poly(dimethylsiloxane)-thermoplastic microfluidic devices at room temperature, Analyst, 145, (21), pp. 6887-6894, 2020. doi: 10.1039/d0an01268h
    [78] M. T. Bakouche, S. Ganesan, D. Guérin, D. Hourlier, M. Bouazaoui, J. P. Vilcot, and S. Maricot, Leak-free integrated microfluidic channel fabrication for surface plasmon resonance applications, J Micromech Microeng, 30, (12), 2020. doi: 10.1088/1361-6439/abb991
    [79] S. R. A. Kratz, B. Bachmann, S. Spitz, G. Holl, C. Eilenberger, H. Goeritz, P. Ertl, and M. Rothbauer, A compression transmission device for the evaluation of bonding strength of biocompatible microfluidic and biochip materials and systems, Sci Rep, 10, (1), p. 1400, 2020. doi: 10.1038/s41598-020-58373-0
    [80] J. Wang, S. Wang, P. Zhang, and Y. Li, Easy-disassembly bonding of PDMS used for leak-tight encapsulation of microfluidic devices, 18th International Conference on Electronic Packaging Technology (ICEPT), pp. 1051-1055, 2017.
    [81] C. f. Chen and K. Wharton, Characterization and failure mode analyses of air plasma oxidized PDMS–PDMS bonding by peel testing, RSC Advances, 7, (3), pp. 1286-1289, 2017. doi: 10.1039/c6ra25947b
    [82] H. Zhang and N. Y. Lee, Non-silicon substrate bonding mediated by poly(dimethylsiloxane) interfacial coating, Appl Surf Sci, 327, pp. 233-240, 2015. doi: 10.1016/j.apsusc.2014.10.172
    [83] H. Dannenberg, Measurement of adhesion by a blister method, J Appl Polym Sci, 5, (14), pp. 125-134, 1961. doi: 10.1002/app.1961.070051401
    [84] X. Chen, C. Shaw, L. Gelman, and K. T. V. Grattan, Advances in test and measurement of the interface adhesion and bond strengths in coating-substrate systems, emphasising blister and bulk techniques, Measurement, 139, pp. 387-402, 2019. doi: 10.1016/j.measurement.2019.03.026
    [85] S. An, D. J. Kang, and A. L. Yarin, A blister-like soft nano-textured thermo-pneumatic actuator as an artificial muscle, Nanoscale, 10, (35), pp. 16591-16600, 2018. doi: 10.1039/c8nr04181d
    [86] M. Lou, I. Abdalla, M. Zhu, J. Yu, Z. Li, and B. Ding, Hierarchically Rough Structured and Self-Powered Pressure Sensor Textile for Motion Sensing and Pulse Monitoring, ACS Appl Mater Interfaces, 12, (1), pp. 1597-1605, 2020. doi: 10.1021/acsami.9b19238
    [87] K. Roy, S. K. Ghosh, A. Sultana, S. Garain, M. Xie, C. R. Bowen, K. Henkel, D. Schmeiβer, and D. Mandal, A Self-Powered Wearable Pressure Sensor and Pyroelectric Breathing Sensor Based on GO Interfaced PVDF Nanofibers, ACS Appl Nano Mater, 2, (4), pp. 2013-2025, 2019. doi: 10.1021/acsanm.9b00033
    [88] A. Wang, M. Hu, L. Zhou, and X. Qiang, Self-Powered Wearable Pressure Sensors with Enhanced Piezoelectric Properties of Aligned P(VDF-TrFE)/MWCNT Composites for Monitoring Human Physiological and Muscle Motion Signs, Nanomaterials (Basel), 8, (12), 2018. doi: 10.3390/nano8121021
    [89] Y. Ni, L. Liu, J. Huang, S. Li, Z. Chen, W. Zhang, and Y. Lai, Rational designed microstructure pressure sensors with highly sensitive and wide detection range performance, J Mater Sci Technol, 130, pp. 184-192, 2022. doi: 10.1016/j.jmst.2022.05.021
    [90] Y. Wang, L. Wang, T. Yang, X. Li, X. Zang, M. Zhu, K. Wang, D. Wu, and H. Zhu, Wearable and Highly Sensitive Graphene Strain Sensors for Human Motion Monitoring, Adv Funct Mater, 24, (29), pp. 4666-4670, 2014. doi: 10.1002/adfm.201400379
    [91] Y. Bar-Cohen, H. Böse, and E. Fuß, Novel dielectric elastomer sensors for compression load detection, presented at the Electroactive Polymer Actuators and Devices (EAPAD) 2014.
    [92] Q. Shu, Z. Xu, S. Liu, J. Wu, H. Deng, X. Gong, and S. Xuan, Magnetic flexible sensor with tension and bending discriminating detection, Chem Eng J, 433, 2022. doi: 10.1016/j.cej.2021.134424
    [93] W. Li, C. Li, G. Zhang, L. Li, K. Huang, X. Gong, C. Zhang, A. Zheng, Y. Tang, Z. Wang, Q. Tong, W. Dong, S. Jiang, S. Zhang, and Q. Wang, Molecular Ferroelectric-Based Flexible Sensors Exhibiting Supersensitivity and Multimodal Capability for Detection, Adv Mater, 33, (44), p. e2104107, 2021. doi: 10.1002/adma.202104107
    [94] A. Bayat, M. Pomplun, and D. A. Tran, A Study on Human Activity Recognition Using Accelerometer Data from Smartphones, Procedia Comput Sci, 34, pp. 450-457, 2014. doi: 10.1016/j.procs.2014.07.009
    [95] Z. Liu, S. Li, J. Hao, J. Hu, M. Pan, and C. Han, An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition, J Adv Transport, 2021, pp. 1-9, 2021. doi: 10.1155/2021/2026895
    [96] L. T. Duan, M. Lawo, Z. G. Wang, and H. Y. Wang, Human Lower Limb Motion Capture and Recognition Based on Smartphones, Sensors (Basel), 22, (14), 2022. doi: 10.3390/s22145273
    [97] Y. Chen and Y. Xue, A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer, presented at the 2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015.
    [98] A. Jalal, S. Kamal, and D. Kim, A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments, Sensors (Basel), 14, (7), pp. 11735-59, 2014. doi: 10.3390/s140711735
    [99] F. J. Ordonez and D. Roggen, Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel), 16, (1), 2016. doi: 10.3390/s16010115
    [100] M. Arif, M. Bilal, A. Kattan, and S. I. Ahamed, Better physical activity classification using smartphone acceleration sensor, J Med Syst, 38, (9), p. 95, 2014. doi: 10.1007/s10916-014-0095-0
    [101] M. Gjoreski, V. Janko, G. Slapničar, M. Mlakar, N. Reščič, J. Bizjak, V. Drobnič, M. Marinko, N. Mlakar, M. Luštrek, and M. Gams, Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors, Inf Fusion, 62, pp. 47-62, 2020. doi: 10.1016/j.inffus.2020.04.004
    [102] H. Yan, A. Bombarely, and S. Li, DeepTE: a computational method for de novo classification of transposons with convolutional neural network, Bioinformatics, 36, (15), pp. 4269-4275, 2020. doi: 10.1093/bioinformatics/btaa519
    [103] S. Subburaj, W.-S. Hung, and P. T. Lin, Measuring the interfacial bonding strength between soft composite material membranes using blister test, Mater Chem Phys, 290, 2022. doi: 10.1016/j.matchemphys.2022.126534
    [104] S. Subburaj, B. Patel, C.-H. Yeh, T.-H. Huang, C.-Y. Chang, W.-S. Hung, and P. T. Lin, Design and fabrication of curved sensor based on polyvinylidene fluoride/graphene composite film with a self-assembling mechanism for monitoring of human body parts movement, Sens Actuator A Phys, 356, 2023. doi: 10.1016/j.sna.2023.114360
    [105] S. Subburaj, C.-H. Yeh, W.-S. Hung, and P. T. Lin, An Innovative Helical Piezoelectric Sensor for Multi-Dimensional Load Detections and Classifications, presented at the 16th Asia Pacific Conference for Non-Destructive Testing (APCNDT 2023), Melbourne, Australia, 2023.
    [106] S. Amarappa and S. Sathyanarayana, Data classification using Support vector Machine (SVM), a simplified approach, Int J Electron Comput Sci Eng, 3, pp. 435-445, 2014.
    [107] L. Breiman, Random forests, Machine learning, 45, pp. 5-32, 2001.
    [108] Y.-W. Lu, P.-T. Lin, and C.-S. Pai, Polydimethylsiloxane (PDMS) bonding strength characterization by a line force model in blister tests, TRANSDUCERS 2007-2007 International Solid-State Sensors, Actuators and Microsystems Conference, pp. 2095-2098, 2007.
    [109] R. Bansal, A textbook of strength of materials:(in SI units). Laxmi Publications, 2010.
    [110] P. Martins, A. C. Lopes, and S. Lanceros-Mendez, Electroactive phases of poly(vinylidene fluoride): Determination, processing and applications, Prog Polym Sci, 39, (4), pp. 683-706, 2014. doi: 10.1016/j.progpolymsci.2013.07.006
    [111] D. Mandal, S. Yoon, and K. J. Kim, Origin of piezoelectricity in an electrospun poly(vinylidene fluoride-trifluoroethylene) nanofiber web-based nanogenerator and nano-pressure sensor, Macromol Rapid Commun, 32, (11), pp. 831-7, 2011. doi: 10.1002/marc.201100040
    [112] T. Lei, X. Cai, X. Wang, L. Yu, X. Hu, G. Zheng, W. Lv, L. Wang, D. Wu, D. Sun, and L. Lin, Spectroscopic evidence for a high fraction of ferroelectric phase induced in electrospun polyvinylidene fluoride fibers, RSC Advances, 3, (47), 2013. doi: 10.1039/c3ra42622j
    [113] J. R. Gregorio and M. Cestari, Effect of crystallization temperature on the crystalline phase content and morphology of poly(vinylidene fluoride), J Polym Sci B Polym Phys, 32, (5), pp. 859-870, 1994. doi: 10.1002/polb.1994.090320509
    [114] S. K. Karan, D. Mandal, and B. B. Khatua, Self-powered flexible Fe-doped RGO/PVDF nanocomposite: an excellent material for a piezoelectric energy harvester, Nanoscale, 7, (24), pp. 10655-66, 2015. doi: 10.1039/c5nr02067k
    [115] M. Sharma, G. Madras, and S. Bose, Contrasting Effects of Graphene Oxide and Poly(ethylenimine) on the Polymorphism in Poly(vinylidene fluoride), Cryst Growth Des, 15, (7), pp. 3345-3355, 2015. doi: 10.1021/acs.cgd.5b00445
    [116] J. Widakdo, Y. H. Chiao, Y. L. Lai, A. C. Imawan, F. M. Wang, and W. S. Hung, Mechanism of a Self-Assembling Smart and Electrically Responsive PVDF-Graphene Membrane for Controlled Gas Separation, ACS Appl Mater Interfaces, 12, (27), pp. 30915-30924, 2020. doi: 10.1021/acsami.0c04402
    [117] W.-S. Hung, S.-Y. Ho, Y.-H. Chiao, C.-C. Chan, W.-Y. Woon, M.-J. Yin, C.-Y. Chang, Y. M. Lee, and Q.-F. An, Electrical Tunable PVDF/Graphene Membrane for Controlled Molecule Separation, Chemistry of Materials, 32, (13), pp. 5750-5758, 2020. doi: 10.1021/acs.chemmater.0c01547
    [118] T. M. Subrahmanya, P. T. Lin, Y.-H. Chiao, J. Widakdo, C.-H. Chuang, S. F. Rahmadhanty, S. Yoshikawa, and W.-S. Hung, High performance self-heated membrane distillation system for energy efficient desalination process, J Mater Chem A, 9, (12), pp. 7868-7880, 2021. doi: 10.1039/d0ta11724b
    [119] D. Damjanovic, Ferroelectric, dielectric and piezoelectric properties of ferroelectric thin films and ceramics, J Reports on progress in physics, 61, (9), p. 1267, 1998.
    [120] T. Hehn and Y. Manoli, CMOS Circuits for Piezoelectric Energy Harvesters: Efficient Power Extraction, Interface Modeling and Loss Analysis, 2015.
    [121] Y. Mao, P. Zhao, G. McConohy, H. Yang, Y. Tong, and X. Wang, Sponge-Like Piezoelectric Polymer Films for Scalable and Integratable Nanogenerators and Self-Powered Electronic Systems, Adv Energy Mater, 4, (7), 2014. doi: 10.1002/aenm.201301624
    [122] C.-Y. Tang, X. Zhao, J. Jia, S. Wang, X.-J. Zha, B. Yin, K. Ke, R.-Y. Bao, Z.-Y. Liu, Y. Wang, K. Zhang, M.-B. Yang, and W. Yang, Low-entropy structured wearable film sensor with piezoresistive-piezoelectric hybrid effect for 3D mechanical signal screening, Nano Energy, 90, 2021. doi: 10.1016/j.nanoen.2021.106603
    [123] C. M. Wu, M. H. Chou, and W. Y. Zeng, Piezoelectric Response of Aligned Electrospun Polyvinylidene Fluoride/Carbon Nanotube Nanofibrous Membranes, Nanomaterials (Basel), 8, (6), 2018. doi: 10.3390/nano8060420
    [124] S. M. Hosseini and A. A. Yousefi, Piezoelectric sensor based on electrospun PVDF-MWCNT-Cloisite 30B hybrid nanocomposites, Org Electron, 50, pp. 121-129, 2017. doi: 10.1016/j.orgel.2017.07.035
    [125] M. Rasoolzadeh, Z. Sherafat, M. Vahedi, and E. Bagherzadeh, Structure dependent piezoelectricity in electrospun PVDF-SiC nanoenergy harvesters, J Alloys Compd, 917, 2022. doi: 10.1016/j.jallcom.2022.165505
    [126] S. P. Timoshenko and J. M. Gere, "Theory of elastic stability. Mineola," ed: New York: Dover Publications, 2009.
    [127] S. Subburaj, C.-H. Yeh, B. Patel, T.-H. Huang, W.-S. Hung, C.-Y. Chang, Y.-W. Wu, and P. T. Lin, K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor, Electronics, 12, (1), 2023. doi: 10.3390/electronics12010210
    [128] L. Xu, W. Yang, Y. Cao, and Q. Li, Human activity recognition based on random forests, 13th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), pp. 548-553, 2017.
    [129] N. Ahmed, J. I. Rafiq, and M. R. Islam, Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model, Sensors (Basel), 20, (1), 2020. doi: 10.3390/s20010317

    無法下載圖示 全文公開日期 2028/08/23 (校內網路)
    全文公開日期 2028/08/23 (校外網路)
    全文公開日期 2028/08/23 (國家圖書館:臺灣博碩士論文系統)
    QR CODE